New Video! How to Pick the Right AI Model for Your GPTs on Pickaxe 🎥

How To Guides
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Hey Pickaxe community!

We’ve got a brand new video from @nathanielmhld that’s all about one of the most common questions we hear: How do you choose the right AI model for your GPT or chatbot?

If you’ve ever stared at that dropdown of models and wondered which one to pick (or why there are so many), this video is for you. Nathaniel breaks it all down, model by model, in plain English.

What’s inside:

:robot: The differences between top models like OpenAI (GPT-4o, 4.1, reasoning vs regular), Claude, Gemini, Mistal, Grok, and DeepSeek

:magnifying_glass_tilted_right: Tips on when to use “reasoning” models and when to stick with regular ones

:high_voltage: Speed, cost, language skills, function calling, guardrails, and other real-world tradeoffs

:light_bulb: How things like content window size or model updates might matter for your project

:hammer_and_wrench: What’s actually best for different types of tools (like chatbots, writing, data lookup, and actions)

:globe_showing_europe_africa: A quick plug on how easy it is to try out and switch models right in Pickaxe (so you can experiment before you commit)

Nathaniel doesn’t just read off a spec sheet. He shares honest experiences, what we’re hearing from real Pickaxe users, and what works (and what doesn’t) for different use cases.

Watch it here:

Whether you’re building your first bot or experimenting with something new, this is a must-watch. If you have questions or want to share what model you love (or hate), jump into the comments and let’s talk!

Happy building,
Abhi,
The Pickaxe Team

5 Likes
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Thanks Abhi! Happy 4th of July!

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My pickaxe relies ENTIRELY on my uploaded Knowledge Base with instructions to the model NOT to seek answers elsewhere. I even instruct the model how to frame its answers. On this basis, which AI models would you recommend to use for the API?.

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Thank you for your question. To ensure your Pickaxe consistently relies on your uploaded Knowledge Base, we recommend adjusting the Relevance Cutoff setting in your Knowledge Settings to a lower value. You can experiment with different values to see which setting works best for your specific needs.

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You may find this blog post helpful for additional guidance:

We also encourage users to write clear and detailed prompts to achieve the most accurate results.

Regarding which model to use, the best choice often depends on your specific use case and the nature of your Knowledge Base files. We suggest testing your setup with different models to determine which one delivers the best results for your application.